





'''simple docstring'''






from manim import *
class 		lowerCamelCase	(   _A							):



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= Rectangle(height=0.5		,   width=0.5						)
			lowerCAmelCase  :     Optional[int]       						= Rectangle(height=0.46		,   width=0.46						).set_stroke(width=0						)

			lowerCAmelCase  :     Optional[Any]       						= Rectangle(height=0.25		,   width=0.25						)

			lowerCAmelCase  :     Union[str, Any]       						= [mem.copy() for i in range(6						)]
			lowerCAmelCase  :     Dict       						= [mem.copy() for i in range(6						)]
			lowerCAmelCase  :     Union[str, Any]       						= VGroup(*a_						).arrange(a_		,   buff=0						)
			lowerCAmelCase  :     int       						= VGroup(*a_						).arrange(a_		,   buff=0						)
			lowerCAmelCase  :     Dict       						= VGroup(a_		,   a_						).arrange(a_		,   buff=0						)
			lowerCAmelCase  :     Union[str, Any]       						= Text("CPU"		,   font_size=24						)
			lowerCAmelCase  :     str       						= Group(a_		,   a_						).arrange(a_		,   buff=0.5		,   aligned_edge=a_						)
			cpu.move_to([-2.5, -0.5, 0]						)
			self.add(a_						)

			lowerCAmelCase  :     Tuple       						= [mem.copy() for i in range(4						)]
			lowerCAmelCase  :     Tuple       						= VGroup(*a_						).arrange(a_		,   buff=0						)
			lowerCAmelCase  :     Optional[int]       						= Text("GPU"		,   font_size=24						)
			lowerCAmelCase  :     Union[str, Any]       						= Group(a_		,   a_						).arrange(a_		,   buff=0.5		,   aligned_edge=a_						)
			gpu.move_to([-1, -1, 0]						)
			self.add(a_						)

			lowerCAmelCase  :     List[Any]       						= [mem.copy() for i in range(6						)]
			lowerCAmelCase  :     Union[str, Any]       						= VGroup(*a_						).arrange(a_		,   buff=0						)

			lowerCAmelCase  :     Optional[Any]       						= Text("Model"		,   font_size=24						)
			lowerCAmelCase  :     str       						= Group(a_		,   a_						).arrange(a_		,   buff=0.5		,   aligned_edge=a_						)
			model.move_to([3, -1.0, 0]						)
			self.add(a_						)

			lowerCAmelCase  :     Dict       						= []
			lowerCAmelCase  :     str       						= []

			for i, rect in enumerate(a_						):
					lowerCAmelCase  :     Union[str, Any]       						= fill.copy().set_fill(a_		,   opacity=0.8						)
					target.move_to(a_						)
					model_arr.append(a_						)

					lowerCAmelCase  :     int       						= Rectangle(height=0.46		,   width=0.46						).set_stroke(width=0.0						).set_fill(a_		,   opacity=0.8						)
					cpu_target.move_to(cpu_left_col_base[i]						)
					model_cpu_arr.append(a_						)

			self.add(*a_		,   *a_						)

			lowerCAmelCase  :     Any       						= [meta_mem.copy() for i in range(6						)]
			lowerCAmelCase  :     Union[str, Any]       						= [meta_mem.copy() for i in range(6						)]
			lowerCAmelCase  :     Union[str, Any]       						= VGroup(*a_						).arrange(a_		,   buff=0						)
			lowerCAmelCase  :     List[Any]       						= VGroup(*a_						).arrange(a_		,   buff=0						)
			lowerCAmelCase  :     Tuple       						= VGroup(a_		,   a_						).arrange(a_		,   buff=0						)
			lowerCAmelCase  :     List[Any]       						= Text("Disk"		,   font_size=24						)
			lowerCAmelCase  :     Optional[int]       						= Group(a_		,   a_						).arrange(a_		,   buff=0.5		,   aligned_edge=a_						)
			disk.move_to([-4, -1.25, 0]						)
			self.add(a_		,   a_						)

			lowerCAmelCase  :     int       						= Square(side_length=2.2						)
			key.move_to([-5, 2, 0]						)

			lowerCAmelCase  :     str       						= MarkupText(
			    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''		,   font_size=18		,   )

			key_text.move_to([-5, 2.4, 0]						)

			self.add(a_		,   a_						)

			lowerCAmelCase  :     int       						= MarkupText(
			    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''		,   font_size=18		,   )

			blue_text.next_to(a_		,   DOWN * 2.4		,   aligned_edge=key_text.get_left()						)
			self.add(a_						)

			lowerCAmelCase  :     Tuple       						= MarkupText(
			    F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.'''		,   font_size=24		,   )
			step_a.move_to([2, 2, 0]						)

			self.play(Write(a_						)						)

			lowerCAmelCase  :     Union[str, Any]       						= Square(0.3						)
			input.set_fill(a_		,   opacity=1.0						)
			input.set_stroke(width=0.0						)
			input.next_to(model_base[0]		,   a_		,   buff=0.5						)

			self.play(Write(a_						)						)

			input.generate_target()
			input.target.next_to(model_arr[0]		,   direction=a_		,   buff=0.02						)
			self.play(MoveToTarget(a_						)						)

			self.play(FadeOut(a_						)						)

			lowerCAmelCase  :     str       						= Arrow(start=a_		,   end=a_		,   color=a_		,   buff=0.5						)
			a.next_to(model_arr[0].get_left()		,   a_		,   buff=0.2						)

			model_cpu_arr[0].generate_target()
			model_cpu_arr[0].target.move_to(gpu_rect[0]						)

			lowerCAmelCase  :     Any       						= MarkupText(
			    F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'''		,   font_size=24		,   )
			step_a.move_to([2, 2, 0]						)

			self.play(Write(a_		,   run_time=3						)						)

			lowerCAmelCase  :     Optional[Any]       						= {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.02}

			self.play(
			    Write(a_						)		,   Circumscribe(model_arr[0]		,   color=a_		,   **a_						)		,   Circumscribe(model_cpu_arr[0]		,   color=a_		,   **a_						)		,   Circumscribe(gpu_rect[0]		,   color=a_		,   **a_						)		,   )
			self.play(MoveToTarget(model_cpu_arr[0]						)						)

			lowerCAmelCase  :     int       						= a.copy()
			for i in range(6						):
					a_c.next_to(model_arr[i].get_right() + 0.02		,   a_		,   buff=0.2						)

					input.generate_target()
					input.target.move_to(model_arr[i].get_right() + 0.02						)

					lowerCAmelCase  :     Dict       						= AnimationGroup(
					    FadeOut(a_		,   run_time=0.5						)		,   MoveToTarget(a_		,   run_time=0.5						)		,   FadeIn(a_		,   run_time=0.5						)		,   lag_ratio=0.2						)

					self.play(a_						)

					model_cpu_arr[i].generate_target()
					model_cpu_arr[i].target.move_to(cpu_left_col_base[i]						)

					if i < 5:
							model_cpu_arr[i + 1].generate_target()
							model_cpu_arr[i + 1].target.move_to(gpu_rect[0]						)
							if i >= 1:
									lowerCAmelCase  :     int       						= 0.7

							self.play(
							    Circumscribe(model_arr[i]		,   **a_						)		,   Circumscribe(cpu_left_col_base[i]		,   **a_						)		,   Circumscribe(cpu_left_col_base[i + 1]		,   color=a_		,   **a_						)		,   Circumscribe(gpu_rect[0]		,   color=a_		,   **a_						)		,   Circumscribe(model_arr[i + 1]		,   color=a_		,   **a_						)		,   )
							if i < 1:
									self.play(
									    MoveToTarget(model_cpu_arr[i]						)		,   MoveToTarget(model_cpu_arr[i + 1]						)		,   )
							else:
									self.play(
									    MoveToTarget(model_cpu_arr[i]		,   run_time=0.7						)		,   MoveToTarget(model_cpu_arr[i + 1]		,   run_time=0.7						)		,   )
					else:
							model_cpu_arr[i].generate_target()
							model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]						)
							input.generate_target()
							input.target.next_to(model_arr[-1].get_right()		,   RIGHT + 0.02		,   buff=0.2						)

							self.play(
							    Circumscribe(model_arr[-1]		,   color=a_		,   **a_						)		,   Circumscribe(cpu_left_col_base[-1]		,   color=a_		,   **a_						)		,   Circumscribe(gpu_rect[0]		,   color=a_		,   **a_						)		,   )

							self.play(MoveToTarget(model_cpu_arr[i]						)						)

					lowerCAmelCase  :     List[str]       						= a_c
					lowerCAmelCase  :     List[Any]       						= a_c.copy()

			input.generate_target()
			input.target.next_to(model_base[-1]		,   RIGHT + 0.02		,   buff=0.5						)
			self.play(
			    FadeOut(a_						)		,   FadeOut(a_		,   run_time=0.5						)		,   )

			lowerCAmelCase  :     str       						= MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.'''		,   font_size=24						)
			step_a.move_to([2, 2, 0]						)

			self.play(Write(a_		,   run_time=3						)		,   MoveToTarget(a_						)						)

			self.wait()










'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "gpt_neox"



	def __init__( self		,   a_=50_432		,   a_=6_144		,   a_=44		,   a_=64		,   a_=24_576		,   a_="gelu"		,   a_=0.25		,   a_=10_000		,   a_=0.0		,   a_=0.0		,   a_=0.1		,   a_=2_048		,   a_=0.02		,   a_=1e-5		,   a_=True		,   a_=0		,   a_=2		,   a_=False		,   a_=True		,   a_=None		,   **a_		,   ):
			super().__init__(bos_token_id=a_		,   eos_token_id=a_		,   **a_						)
			lowerCAmelCase  :     Dict       						= vocab_size
			lowerCAmelCase  :     str       						= max_position_embeddings
			lowerCAmelCase  :     int       						= hidden_size
			lowerCAmelCase  :     Optional[Any]       						= num_hidden_layers
			lowerCAmelCase  :     int       						= num_attention_heads
			lowerCAmelCase  :     Any       						= intermediate_size
			lowerCAmelCase  :     Tuple       						= hidden_act
			lowerCAmelCase  :     Any       						= rotary_pct
			lowerCAmelCase  :     str       						= rotary_emb_base
			lowerCAmelCase  :     Tuple       						= attention_dropout
			lowerCAmelCase  :     Optional[int]       						= hidden_dropout
			lowerCAmelCase  :     int       						= classifier_dropout
			lowerCAmelCase  :     str       						= initializer_range
			lowerCAmelCase  :     str       						= layer_norm_eps
			lowerCAmelCase  :     List[Any]       						= use_cache
			lowerCAmelCase  :     Dict       						= tie_word_embeddings
			lowerCAmelCase  :     Any       						= use_parallel_residual
			lowerCAmelCase  :     Union[str, Any]       						= rope_scaling
			self._rope_scaling_validation()

			if self.hidden_size % self.num_attention_heads != 0:
					raise ValueError(
					    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"						)





	def 	_lowerCamelCase     ( self						):
			if self.rope_scaling is None:
					return

			if not isinstance(self.rope_scaling		,   a_						) or len(self.rope_scaling						) != 2:
					raise ValueError(
					    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
					    F'''got {self.rope_scaling}'''						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("type"		,   a_						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("factor"		,   a_						)
			if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
					raise ValueError(
					    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''						)
			if rope_scaling_factor is None or not isinstance(a_		,   a_						) or rope_scaling_factor <= 1.0:
					raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''						)



